BR
BrillioSenior Data Science Lead - R01551335
LocationChennai, Tamil Nadu, India
Work modehybrid
SeniorityLead
DepartmentAI & Data Engineering, AI & Data Engineering : Data Science
Company size5,001–10,000 people
PostedJun 5, 2025 · 1y ago
Verified live1d ago
At a glanceSummarised by Seekless from the posting.
Hands-on experience with LangGraph
Deep expertise in LLM orchestration
Deep expertise in knowledge graphs
Deep expertise in reinforcement learning (RLHF/RLAIF)
Deep expertise in real-world AI applications
Lead initiatives to build multi-agent AI systems
Design and develop multi-agent AI systems using LangGraph for workflow automation, complex decision-making, and autonomous problem-solving
Build memory-augmented, context-aware AI agents capable of planning, reasoning, and executing tasks across multiple domains
Define and implement scalable architectures for LLM-powered agents that seamlessly integrate with enterprise applications
Develop and optimize agent orchestration workflows using LangGraph, ensuring high performance, modularity, and scalability
Implement knowledge graphs, vector databases (Pinecone, Weaviate, FAISS), and retrieval-augmented generation (RAG) techniques for enhanced agent reasoning
Apply reinforcement learning (RLHF/RLAIF) methodologies to fine-tune AI agents for improved decision-making
Lead cutting-edge AI research in Agentic AI, LangGraph, LLM Orchestration, and Self-improving AI Agents
Stay ahead of advancements in multi-agent systems, AI planning, and goal-directed behavior, applying best practices to enterprise AI solutions
Prototype and experiment with self-learning AI agents, enabling autonomous adaptation based on real-time feedback loops
Translate Agentic AI capabilities into enterprise solutions, driving automation, operational efficiency, and cost savings
Lead Agentic AI proof-of-concept (PoC) projects that demonstrate tangible business impact and scale successful prototypes into production
Lead and mentor a team of AI Engineers and Data Scientists, fostering deep technical expertise in LangGraph and multi-agent architectures
Establish best practices for model evaluation, responsible AI, and real-world deployment of autonomous AI agents
Skills
Hypothesis Testing
T-Test
Z-Test
Regression (Linear, Logistic)
Python
PySpark
SAS
SPSS
Statistical analysis and computing
Probabilistic Graph Models
Great Expectation
Evidently AI
Forecasting (Exponential Smoothing, ARIMA, ARIMAX)
KubeFlow
BentoML
Classification (Decision Trees, SVM)
TensorFlow
PyTorch
Sci-Kit Learn
CNTK
Keras
MXNet
Distance (Hamming Distance, Euclidean Distance, Manhattan Distance)
R
R Studio
LangGraph
LLM orchestration
knowledge graphs
reinforcement learning
RLHF
RLAIF
vector databases
Pinecone
Weaviate
FAISS
RAG
Senior Data Science Lead
Primary Skills
•
Hypothesis Testing, T-Test, Z-Test, Regression (Linear, Logistic), Python/PySpark, SAS/SPSS, Statistical analysis and computing, Probabilistic Graph Models, Great Expectation, Evidently AI, Forecasting (Exponential Smoothing, ARIMA, ARIMAX), Tools(KubeFlow, BentoML), Classification (Decision Trees, SVM), ML Frameworks (TensorFlow, PyTorch, Sci-Kit Learn, CNTK, Keras, MXNet), Distance (Hamming Distance, Euclidean Distance, Manhattan Distance), R/ R Studio
Job requirements
•
JD is below: The Agentic AI Lead is a pivotal role responsible for driving the research, development, and deployment of semi-autonomous AI agents to solve complex enterprise challenges. This role involves hands-on experience with LangGraph, leading initiatives to build multi-agent AI systems that operate with greater autonomy, adaptability, and decision-making capabilities. The ideal candidate will have deep expertise in LLM orchestration, knowledge graphs, reinforcement learning (RLHF/RLAIF), and real-world AI applications. As a leader in this space, they will be responsible for designing, scaling, and optimizing agentic AI workflows, ensuring alignment with business objectives while pushing the boundaries of next-gen AI automation. ________________________________________ Key Responsibilities 1. Architecting & Scaling Agentic AI Solutions • Design and develop multi-agent AI systems using LangGraph for workflow automation, complex decision-making, and autonomous problem-solving. • Build memory-augmented, context-aware AI agents capable of planning, reasoning, and executing tasks across multiple domains. • Define and implement scalable architectures for LLM-powered agents that seamlessly integrate with enterprise applications. 2. Hands-On Development & Optimization • Develop and optimize agent orchestration workflows using LangGraph, ensuring high performance, modularity, and scalability. • Implement knowledge graphs, vector databases (Pinecone, Weaviate, FAISS), and retrieval-augmented generation (RAG) techniques for enhanced agent reasoning. • Apply reinforcement learning (RLHF/RLAIF) methodologies to fine-tune AI agents for improved decision-making. 3. Driving AI Innovation & Research • Lead cutting-edge AI research in Agentic AI, LangGraph, LLM Orchestration, and Self-improving AI Agents. • Stay ahead of advancements in multi-agent systems, AI planning, and goal-directed behavior, applying best practices to enterprise AI solutions. • Prototype and experiment with self-learning AI agents, enabling autonomous adaptation based on real-time feedback loops. 4. AI Strategy & Business Impact • Translate Agentic AI capabilities into enterprise solutions, driving automation, operational efficiency, and cost savings. • Lead Agentic AI proof-of-concept (PoC) projects that demonstrate tangible business impact and scale successful prototypes into production. 5. Mentorship & Capability Building • Lead and mentor a team of AI Engineers and Data Scientists, fostering deep technical expertise in LangGraph and multi-agent architectures. • Establish best practices for model evaluation, responsible AI, and real-world deployment of autonomous AI agents. ________________________________________